NYC Data Science Academy, one of the leading data science bootcamps in the country, has announced new courses for Machine Learning in Finance, specifically tailored for traders, risk managers, portfolio managers, investors, and those looking to build data science skills to work in the finance industry.
Watch an interview with instructors:
Machine learning is a type of artificial intelligence (AI) where computers can essentially learn concepts on their own without being programmed. These are computer programs that alter their “thinking” (or output) once exposed to new data. For machine learning to take place, algorithms are needed. Algorithms are put into the computer and give it rules to follow when dissecting data.
When applied to the field of finance, Machine Learning can be used to enhance portfolio optimization, and logistic regression combined with clustering algorithms can be used to not only predict risk of default, but more importantly, back test and manage a loan portfolio.
“Being in New York, at the center of the world’s financial system, it makes perfect sense that we develop a specific course for applying the latest technologies of Machine Learning to the world of finance,” said Vivian Zhang, founder and CTO of the NYC Data Science Academy. “We strive to provide the most useful applicable courses and curriculum in data science and we are excited about this new offering specifically for the world of finance.”
The Machine Learning in Finance course is a dense presentation of machine learning (ML) tools used in financial risk management, portfolio management, and trading. Ten classes are offered: two on risk management, two on loan portfolio management, three on portfolio optimization, and three on high-frequency trading. The course will be taught by:
David Romoff - a risk management consultant with 10 years of experience modeling market and credit risk using the latest methods and technologies. David's recent work includes serving as Manager of Risk Management at On Deck Capital, a business lending company in the FinTech space that uses machine learning models to underwrite loans. He is also teaching graduate courses in Financial Risk Management at Columbia University.
Jing Guo - a quantitative associate in Goldman Sachs’ Macroeconomic Team. Before joining Goldman Sachs, he was an Algo Trading researcher at Guggenheim Partners. He holds an MS degree in statistics from University of Virginia and Ph.D degree in Financial Engineering from Columbia University.
Wes Aull - a CPA/ABV who works as portfolio manager & data scientist at JTW Capital. His research interests include fundamental analysis, game theory, network science, data visualization, directional statistics, and algorithms relevant to nonlinear/periodic function approximation. He earned his M.B.A. from Columbia Business School, Master’s of Professional Accounting from University of Texas-Austin, and B.S. Mathematical Economics from University of Kentucky.
The first course begins on February 21, 2018, and meets 10 times over the coming month, concluding on March 26.
To learn more about these courses click here.
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This content was sponsored by NYC Data Science Academy